Julien Rebut
- Aerospace Engineering top 10%
- Computer Vision and Pattern Recognition top 10%
- Automotive Engineering
- Ocean Engineering
- Environmental Engineering
- Co-authors
- Patrick PérezFlorence TupinAlasdair NewsonAbdelaziz BensrhairGwenaëlle ToulminetAndrei BursucRobert LaganièreWaqas Ahmed Malik
- Topics
- Autonomous Vehicle Technology and Safety (5 papers)Advanced Optical Sensing Technologies (4 papers)Advanced Neural Network Applications (3 papers)
- Journals
- SAE technical papers on CD-ROM/SAE technical paper series2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
In The Last Decade
Julien Rebut
9 papers receiving 225 citations
Peers
Comparison fields: 5 of 41
- Aerospace Engineering 118
- Computer Vision and Pattern Recognition 107
- Automotive Engineering 53
- Ocean Engineering 40
- Environmental Engineering 38
Countries citing papers authored by Julien Rebut
This map shows the geographic impact of Julien Rebut's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Julien Rebut with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julien Rebut more than expected).
Fields of papers citing papers by Julien Rebut
This network shows the impact of papers produced by Julien Rebut. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Julien Rebut. The network helps show where Julien Rebut may publish in the future.
Co-authorship network of co-authors of Julien Rebut
This figure shows the co-authorship network connecting the top 25 collaborators of Julien Rebut. A scholar is included among the top collaborators of Julien Rebut based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Julien Rebut. Julien Rebut is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 65 | |
| 2 | 91 | |
| 3 | 1 | |
| 4 | 8 | |
| 5 | 3 | |
| 6 | 15 | |
| 7 | 2 | |
| 8 | 28 | |
| 9 | 13 |
About Julien Rebut
Julien Rebut is a scholar working on Instrumentation, Automotive Engineering and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 226 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (5 papers), Advanced Optical Sensing Technologies (4 papers) and Advanced Neural Network Applications (3 papers). The work is most often cited by research in Instrumentation (27 citations), Computer Vision and Pattern Recognition (107 citations) and Aerospace Engineering (118 citations). Julien Rebut has collaborated with scholars based in France and Canada. Frequent co-authors include Patrick Pérez, Florence Tupin, Alasdair Newson, Abdelaziz Bensrhair, Gwenaëlle Toulminet, Andrei Bursuc, Robert Laganière and Waqas Ahmed Malik. Their work appears in journals such as SAE technical papers on CD-ROM/SAE technical paper series, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.